Zero Data Retention

Zero Data Retention is a privacy-oriented inference offering that frames its value around how requests are handled rather than around a new architecture or training run, treating each prompt and completion as ephemeral so that no prompt or response payload is stored once the call returns. In practice, this positions the offering for teams that want to route sensitive workloads through a third party while keeping the data-handling footprint as small as possible, since the underlying principle is that nothing the user sends or receives should persist on the provider side after processing. The trade-off, as discussed in third-party coverage of zero data retention policies, is that any conveniences that depend on retained logs, such as historical conversation recall, debugging replays, or fine-tuning from prior traffic, are unavailable because the data is simply not kept.

Because the offering is defined by a retention posture rather than a novel model family, its practical fit is best understood through use cases: regulated industries that need defensible data minimization, internal tools where logs cannot leave the organization, and workflows that already wrap calls in their own audit layer. Users evaluating it should verify the specific retention contract in place, since broader secondary coverage notes that commitments can vary across providers and that operational exceptions sometimes apply even under a zero-retention banner. The offering is therefore most attractive as a complement to existing privacy controls, giving teams an inference path whose privacy guarantees are stated up front rather than inferred from defaults.